A tag is a basis for a user to express a field demand, and is also a basis for forming the user's query. When the user uses a search engine to perform search, he uses a query to express a search intention. However, insufficient recall results are often caused because the user-selected query does not match search results. The problem is particularly serious in the vertical field. Take the field of movies as an example. When the user searches for “brain-burning movies”, he really intends to search for “high IQ movies” or “suspense and reasoning-type movies”. If the search engine does not build a mapping relationship between “brain-burning movies” with “high IQ”, “suspense” and “reasoning”-type move resources, search results call will be caused insufficient.
Therefore, to enable search results in a synonymous relationship with the user-provided query to be recalled simultaneously, it is necessary to perform synonymous query expansion based on the user-provided query, i.e., while using the tag included by the query to search, it is also necessary to further use the synonymy tags of the tag to search, thereby achieving a purpose of meeting the user's real intention and enhancing the user's experience and maximized business value. At present, mining of synonym tags is handled as a phrase paraphrase task, which depends on mining resources such as a click log having a paraphrase relationship or data having bilingual parallel alignment corpus.
However, since the current mining of synonymy tags depends on mining resources such as a click log having a paraphrase relationship or data having bilingual parallel alignment corpus, is some cases, for example, the data of mining resources is sparse, a coverage rate of synonymy tags will be made lower and therefore a reliability of obtaining synonymy tags will be caused lower.